Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control

Orsborn, Amy L.; Moorman, Helene G.; Overduin, Simon A.; Shanechi, Maryam M.; Dimitrov, D; Carmena, Jose M.

doi:10.1016/j.neuron.2014.04.048

Cited by 234 publications

(298 citation statements)

References 32 publications

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“…This concept of simultaneous (or co-adaptive) learning has been proven to be effective also in the context of brain-computer-interfaces ( [17], [18], [26], [27]). Such co-adaptive systems are influenced by the speed with which both learners, the human and the machine, are adapting [18].…”

Section: Introductionmentioning

confidence: 99%

Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Hahne

Dähne

Hwang

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

121

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Myoelectric control of a prosthetic hand with more than one degree of freedom (DoF) is challenging, and clinically available techniques require a sequential actuation of the DoFs. Simultaneous and proportional control of multiple DoFs is possible with regression-based approaches allowing for fluent and natural movements. Conventionally, the regressor is calibrated in an open-loop with training based on recorded data and the performance is evaluated subsequently. For individuals with amputation or congenital limb-deficiency who need to (re)learn how to generate suitable muscle contractions, this open-loop process may not be effective. We present a closed-loop real-time learning scheme in which both the user and the machine learn simultaneously to follow a common target. Experiments with ten able-bodied individuals show that this co-adaptive closed-loop learning strategy leads to significant performance improvements compared to a conventional open-loop training paradigm. Importantly, co-adaptive learning allowed two individuals with congenital deficiencies to perform simultaneous 2-D proportional control at levels comparable to the able-bodied individuals, despite having to a learn completely new and unfamiliar mapping from muscle activity to movement trajectories. To our knowledge, this is the first study which investigates man-machine co-adaptation for regression-based myoelectric control. The proposed training strategy has the potential to improve myographic prosthetic control in clinically relevant settings.

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Section: Introductionmentioning

confidence: 99%

Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Hahne

Dähne

Hwang

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

121

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“…While there has been substantial work in developing adaptive BMI decoders that update their kinematic decoding parameters in response to externally specified errors [31,50,60–63] or inferred errors from the statistics of the system’s output [64,65], intracortical BMI designs have not explored the utility of a biological task outcome error signal. A BMI user is typically provided constant visual feedback of the BMI-controlled effector (e.g., computer cursor) and the BMI behavioral goal (such as the target on the screen), and is therefore aware of their BMI performance.…”

mentioning

confidence: 99%

Augmenting intracortical brain-machine interface with neurally driven error detectors

et al. 2017

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Objective Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error “detect-and-act” system that attempts to automatically “undo” or “prevent” mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

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“…This goes back to the fundamental questions about the nature of plasticity raised by Hebb and John, reviewed above. BMI experiments can spectacularly manipulate cellularto-population tuning curves, but the role of plasticity at the synaptic-to-cellular level (i.e., physical changes in synaptic structure) in these manipulations largely remains to be investigated (Legenstein et al, 2010;Koralek et al, 2012;Orsborn and Carmena, 2013;Orsborn et al, 2014).…”

Section: Bmi To Study Plasticitymentioning

confidence: 99%

Brain-Machine Interfaces beyond Neuroprosthetics

Moxon

Foffani

2015

Neuron

112

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The field of invasive brain-machine interfaces (BMIs) is typically associated with neuroprosthetic applications aiming to recover loss of motor function. However, BMIs also represent a powerful tool to address fundamental questions in neuroscience. The observed subjects of BMI experiments can also be considered as indirect observers of their own neurophysiological activity, and the relationship between observed neurons and (artificial) behavior can be genuinely causal rather than indirectly correlative. These two characteristics defy the classical object-observer duality, making BMIs particularly appealing for investigating how information is encoded and decoded by neural circuits in real time, how this coding changes with physiological learning and plasticity, and how it is altered in pathological conditions. Within neuroengineering, BMI is like a tree that opens its branches into many traditional engineering fields, but also extends deep roots into basic neuroscience beyond neuroprosthetics.

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Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control

Cited by 234 publications

References 32 publications

Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Augmenting intracortical brain-machine interface with neurally driven error detectors

Brain-Machine Interfaces beyond Neuroprosthetics

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